0.13.0Returns accuracy.
number:
Accuracy
Returns precision with macro average.
number:
Precision
Returns recall with macro average.
number:
Recall
Returns F-score with macro average.
number:
F-score
Returns Cohen's kappa coefficient.
number:
Cohen's kappa coefficient
Returns Davies-Bouldin index.
number:
Davies-Bouldin index
Returns Silhouette coefficient.
Array<number>:
Silhouette coefficient
Returns Dunn index.
"max" | "mean" | "centroid"), inter_d: "centroid"): number(Array<any>)
Predicted categories
(("max" | "mean" | "centroid")
= 'max')
Intra-cluster distance type
("centroid"
= 'centroid')
Inter-cluster distance type
number:
Dunn index
Returns Purity.
number:
Purity
Returns Rand index.
number:
Rank index
Returns Dice index.
number:
Dice index
Returns Jaccard index.
number:
Jaccard index
Returns Fowlkes-Mallows index.
number:
Fowlkes-Mallows index
Returns Co-Ranking Matrix.
number:
Co-Ranking Matrix value
Returns MSE (Mean Squared Error).
(number | Array<number>):
Mean Squared Error
Returns RMSE (Root Mean Squared Error).
(number | Array<number>):
Root Mean Squared Error
Returns MAE (Mean Absolute Error).
(number | Array<number>):
Mean Absolute Error
Returns MAD (Median Absolute Deviation).
(number | Array<number>):
Median Absolute Deviation
Returns RMSPE (Root Mean Squared Percentage Error).
(number | Array<number>):
Root Mean Squared Percentage Error
Returns MAPE (Mean Absolute Percentage Error).
(number | Array<number>):
Mean Absolute Percentage Error
Returns MSLE (Mean Squared Logarithmic Error).
(number | Array<number>):
Mean Squared Logarithmic Error
Returns RMSLE (Root Mean Squared Logarithmic Error).
(number | Array<number>):
RootMean Squared Logarithmic Error
Returns R2 (coefficient of determination).
(number | Array<number>):
Coefficient of determination
Returns correlation.
(number | Array<number>):
Correlation
Default export object.
(Tensor)
: Tensor class
(Matrix)
: Matrix class
(Graph)
: Graph class
(Complex)
: Complex number
(A2CAgent)
: A2C agent
(ADALINE)
: Adaptive Linear Neuron model
(ADAMENN)
: Adaptive Metric Nearest Neighbor
(AdaptiveThresholding)
: Adaptive thresholding
(AffinityPropagation)
: Affinity propagation model
(CentroidAgglomerativeClustering)
: Centroid agglomerative clustering
(CompleteLinkageAgglomerativeClustering)
: Complete linkage agglomerative clustering
(GroupAverageAgglomerativeClustering)
: Group average agglomerative clustering
(MedianAgglomerativeClustering)
: Median agglomerative clustering
(SingleLinkageAgglomerativeClustering)
: Single linkage agglomerative clustering
(WardsAgglomerativeClustering)
: Ward's agglomerative clustering
(WeightedAverageAgglomerativeClustering)
: Weighted average agglomerative clustering
(AkimaInterpolation)
: Akima interpolation
(ALMA)
: Approximate Large Margin algorithm
(AODE)
: Averaged One-Dependence Estimators
(AR)
: Autoregressive model
(ARMA)
: Autoregressive moving average model
(AROW)
: Adaptive regularization of Weight Vectors
(ART)
: Adaptive resonance theory
(AssociationAnalysis)
: Association analysis
(Autoencoder)
: Autoencoder
(AutomaticThresholding)
: Automatic thresholding
(AverageShiftedHistogram)
: Average shifted histogram
(BalancedHistogramThresholding)
: Balanced histogram thresholding
(Ballseptron)
: Ballseptron
(Banditron)
: Banditron
(BayesianLinearRegression)
: Bayesian linear regression
(BayesianNetwork)
: Bayesian Network
(BernsenThresholding)
: Bernsen thresholding
(BesselFilter)
: Bessel filter
(BilinearInterpolation)
: Bilinear interpolation
(BIRCH)
: Balanced iterative reducing and clustering using hierarchies
(BoxCox)
: Box-Cox transformation
(BrahmaguptaInterpolation)
: Brahmagupta interpolation
(BSGD)
: Budgeted Stochastic Gradient Descent
(BudgetPerceptron)
: Budget Perceptron
(ButterworthFilter)
: Butterworth filter
(C2P)
: Clustering based on Closest Pairs
(Canny)
: Canny edge detection
(CAST)
: Clustering Affinity Search Technique
(CategoricalNaiveBayes)
: Categorical naive bayes
(CatmullRomSplines)
: Catmull-Rom splines interpolation
(CentripetalCatmullRomSplines)
: Centripetal Catmull-Rom splines interpolation
(CHAMELEON)
: CHAMELEON
(ChangeFinder)
: Change finder
(ChebyshevFilter)
: Chebyshev filter
(CLARA)
: Clustering LARge Applications
(CLARANS)
: Clustering Large Applications based on RANdomized Search
(CLUES)
: CLUstEring based on local Shrinking
(CoTraining)
: Co-training
(COF)
: Connectivity-based Outlier Factor
(ComplementNaiveBayes)
: Complement Naive Bayes
(ConfidenceWeighted)
: Confidence weighted
(SoftConfidenceWeighted)
: Soft confidence weighted
(CosineInterpolation)
: Cosine interpolation
(CRF)
: Conditional random fields
(CubicConvolutionInterpolation)
: Cubic-convolution interpolation
(CubicHermiteSpline)
: Cubic Hermite spline
(CubicInterpolation)
: Cubic interpolation
(CumulativeMovingAverage)
: Cumulative moving average
(CumSum)
: Cumulative sum change point detection
(CURE)
: Clustering Using REpresentatives
(DiscriminantAdaptiveNearestNeighbor)
: Discriminant adaptive nearest neighbor
(DBSCAN)
: Density-based spatial clustering of applications with noise
(DecisionTreeClassifier)
: Decision tree classifier
(DecisionTreeRegression)
: Decision tree regression
(DelaunayInterpolation)
: Delaunay interpolation
(DemingRegression)
: Deming regression
(DENCLUE)
: DENsity CLUstering
(DIANA)
: DIvisive ANAlysis Clustering
(DiffusionMap)
: Diffusion map
(DQNAgent)
: Deep Q-Network agent
(DPAgent)
: Dynamic programming agent
(ElasticNet)
: Elastic net
(EllipticFilter)
: Elliptic filter
(ENN)
: Extended Nearest Neighbor
(EnsembleBinaryModel)
: Ensemble binary models
(ExponentialMovingAverage)
: Exponential moving average
(ModifiedMovingAverage)
: Modified moving average
(ExtraTreesClassifier)
: Extra trees classifier
(ExtraTreesRegressor)
: Extra trees regressor
(FastMap)
: FastMap
(Forgetron)
: Forgetron
(FuzzyCMeans)
: Fuzzy c-means
(FuzzyKNN)
: Fuzzy k-nearest neighbor
(GAN)
: Generative adversarial networks
(GasserMuller)
: Gasser–Müller kernel estimator
(GaussianProcess)
: Gaussian process
(GBDT)
: Gradient boosting decision tree
(GBDTClassifier)
: Gradient boosting decision tree classifier
(GeneralizedESD)
: Generalized extreme studentized deviate
(GeneticAlgorithmGeneration)
: Genetic algorithm generation
(GeneticKMeans)
: Genetic k-means model
(GMeans)
: G-means
(GMM)
: Gaussian mixture model
(GMR)
: Gaussian mixture regression
(SemiSupervisedGMM)
: Semi-Supervised gaussian mixture model
(GPLVM)
: Gaussian Process Latent Variable Model
(GrowingCellStructures)
: Growing cell structures
(GrowingNeuralGas)
: Growing neural gas
(GTM)
: Generative topographic mapping
(HampelFilter)
: Hampel filter
(HDBSCAN)
: Hierarchical Density-based spatial clustering of applications with noise
(Histogram)
: Histogram
(HLLE)
: Hessian Locally Linear Embedding
(ContinuousHMM)
: Continuous hidden Markov model
(HMM)
: Hidden Markov model
(HMMClassifier)
: Hidden Markov model classifier
(HoltWinters)
: Holt-Winters method
(HopfieldNetwork)
: Hopfield network
(Hotelling)
: Hotelling T-square Method
(HuberRegression)
: Huber regression
(ICA)
: Independent component analysis
(CELLIP)
: Classical ellipsoid method
(IELLIP)
: Improved ellipsoid method
(IKNN)
: Locally Informative K-Nearest Neighbor
(IncrementalPCA)
: Incremental principal component analysis
(INFLO)
: Influenced Outlierness
(InverseDistanceWeighting)
: Inverse distance weighting
(InverseSmoothstepInterpolation)
: Inverse smoothstep interpolation
(ISODATA)
: Iterative Self-Organizing Data Analysis Technique
(IsolationForest)
: Isolation forest
(Isomap)
: Isomap
(IsotonicRegression)
: Isotonic regression
(KalmanFilter)
: Kalman filter
(KDEOS)
: Kernel Density Estimation Outlier Score
(KernelDensityEstimator)
: Kernel density estimator
(KernelKMeans)
: Kernel k-means
(KernelizedPegasos)
: Kernelized Primal Estimated sub-GrAdientSOlver for SVM
(KernelizedPerceptron)
: Kernelized perceptron
(KLIEP)
: Kullback-Leibler importance estimation procedure
(KMeans)
: k-means model
(KMeanspp)
: k-means++ model
(KMedians)
: k-medians model
(KMedoids)
: k-medoids model
(SemiSupervisedKMeansModel)
: semi-supervised k-means model
(KModes)
: k-modes model
(KNN)
: k-nearest neighbor
(KNNAnomaly)
: k-nearest neighbor anomaly detection
(KNNDensityEstimation)
: k-nearest neighbor density estimation
(KNNRegression)
: k-nearest neighbor regression
(SemiSupervisedKNN)
: Semi-supervised k-nearest neighbor
(KPrototypes)
: k-prototypes model
(KSVD)
: k-SVD
(KolmogorovZurbenkoFilter)
: Kolmogorov–Zurbenko filter
(LabelPropagation)
: Label propagation
(LabelSpreading)
: Label spreading
(LadderNetwork)
: Ladder network
(LagrangeInterpolation)
: Lagrange interpolation
(LanczosInterpolation)
: Lanczos interpolation
(Laplacian)
: Laplacian edge detection
(LaplacianEigenmaps)
: Laplacian eigenmaps
(Lasso)
: Least absolute shrinkage and selection operator
(LatentDirichletAllocation)
: Latent dirichlet allocation
(LBG)
: Linde-Buzo-Gray algorithm
(FishersLinearDiscriminant)
: Fishers linear discriminant analysis
(LinearDiscriminant)
: Linear discriminant analysis
(LinearDiscriminantAnalysis)
: Linear discriminant analysis
(MulticlassLinearDiscriminant)
: Multiclass linear discriminant analysis
(LDF)
: Local Density Factor
(LDOF)
: Local Distance-based Outlier Factor
(LeastAbsolute)
: Least absolute deviations
(LeastSquares)
: Least squares
(LinearInterpolation)
: Linear interpolation
(LLE)
: Locally Linear Embedding
(LeastMedianSquaresRegression)
: Least median squares regression
(LMNN)
: Large Margin Nearest Neighbor
(LOCI)
: Local Correlation Integral
(LOF)
: Local Outlier Factor
(LoG)
: Laplacian of gaussian filter
(LogarithmicInterpolation)
: Logarithmic interpolation
(LogisticRegression)
: Logistic regression
(MultinomialLogisticRegression)
: Multinomial logistic regression
(LoOP)
: Local Outlier Probability
(LOWESS)
: Locally weighted scatter plot smooth
(LowpassFilter)
: Lowpass filter
(LpNormLinearRegression)
: Lp norm linear regression
(LSA)
: Latent Semantic Analysis
(LSDD)
: Least-squares density difference
(LSDDCPD)
: LSDD for change point detection
(LSIF)
: least-squares importance fitting
(LeastTrimmedSquaresRegression)
: Least trimmed squares
(LVQClassifier)
: Learning Vector Quantization classifier
(LVQCluster)
: Learning Vector Quantization clustering
(MAD)
: Median Absolute Deviation
(MarginPerceptron)
: Margin Perceptron
(MarkovSwitching)
: Markov switching
(MaxAbsScaler)
: Max absolute scaler
(MaximumLikelihoodEstimator)
: Maximum likelihood estimator
(MCD)
: Minimum Covariance Determinant
(MixtureDiscriminant)
: Mixture discriminant analysis
(MDS)
: Multi-dimensional Scaling
(MeanShift)
: Mean shift
(MetropolisHastings)
: Metropolis-Hastings algorithm
(MinmaxNormalization)
: Min-max normalization
(MIRA)
: Margin Infused Relaxed Algorithm
(MLLE)
: Modified Locally Linear Embedding
(MLPClassifier)
: Multi layer perceptron classifier
(MLPRegressor)
: Multi layer perceptron regressor
(MOD)
: Method of Optimal Direction
(MONA)
: MONothetic Analysis Clustering
(MonotheticClustering)
: Monothetic Clustering
(MCAgent)
: Monte Carlo agent
(Mountain)
: Mountain method
(LinearWeightedMovingAverage)
: Linear weighted moving average
(SimpleMovingAverage)
: Simple moving average
(TriangularMovingAverage)
: Triangular moving average
(MovingMedian)
: Moving median
(MT)
: Mahalanobis Taguchi method
(MutualInformationFeatureSelection)
: Mutual information feature selector
(MutualKNN)
: Mutual k-nearest-neighbor model
(NCubicInterpolation)
: n-cubic interpolation
(NLinearInterpolation)
: n-linear interpolation
(NadarayaWatson)
: Nadaraya–Watson kernel regression
(NaiveBayes)
: Naive bayes
(NAROW)
: Narrow Adaptive Regularization Of Weights
(NaturalNeighborInterpolation)
: Natural neighbor interpolation
(NeighbourhoodComponentsAnalysis)
: Neighbourhood components analysis
(NearestCentroid)
: Nearest centroid classifier
(NegationNaiveBayes)
: Negation Naive bayes
(NeuralGas)
: Neural gas model
(ComputationalGraph)
(Layer)
(NeuralnetworkException)
: Exception for neuralnetwork class
(NeuralNetwork)
: Neuralnetwork
(NiblackThresholding)
: Niblack thresholding
(NICE)
: Flow-based generative model non-linear independent component estimation
(NLMeans)
: Non-local means filter
(NMF)
: Non-negative matrix factorization
(NNBCA)
: Natural Neighborhood Based Classification Algorithm
(NormalHERD)
: Normal Herd
(OCSVM)
: One-class support vector machine
(ODIN)
: Outlier Detection using Indegree Number
(OnlineGradientDescent)
: Online gradient descent
(OPTICS)
: Ordering points to identify the clustering structure
(OtsusThresholding)
: Otus's thresholding
(PAM)
: Partitioning Around Medoids
(ParticleFilter)
: Particle filter
(PassingBablok)
: Passing-Bablok method
(PA)
: Passive Aggressive
(PAUM)
: Perceptron Algorithm with Uneven Margins
(AnomalyPCA)
: Principal component analysis for anomaly detection
(DualPCA)
: Dual Principal component analysis
(KernelPCA)
: Kernel Principal component analysis
(PCA)
: Principal component analysis
(PossibilisticCMeans)
: Possibilistic c-means
(PCR)
: Principal component regression
(Pegasos)
: Primal Estimated sub-GrAdientSOlver for SVM
(PercentileAnormaly)
: Percentile anomaly detection
(AveragedPerceptron)
: Averaged perceptron
(MulticlassPerceptron)
: Multiclass perceptron
(Perceptron)
: Perceptron
(PhansalkarThresholding)
: Phansalkar thresholding
(PLS)
: Partial least squares regression
(PLSA)
: Probabilistic latent semantic analysis
(PoissonRegression)
: Poisson regression
(PGAgent)
: Policy gradient agent
(PolynomialHistogram)
: Polynomial histogram
(PolynomialInterpolation)
: Polynomial interpolation
(ProjectionPursuit)
: Projection pursuit regression
(Prewitt)
: Prewitt edge detection
(PriestleyChao)
: Priestley–Chao kernel estimator
(PrincipalCurve)
: Principal curves
(ProbabilisticPCA)
: Probabilistic Principal component analysis
(MultinomialProbit)
: Multinomial probit
(Probit)
: Probit
(Projectron)
: Projectron
(Projectronpp)
: Projectron++
(PTile)
: P-tile thresholding
(QTableBase)
: Base class for Q-table
(QAgent)
: Q-learning agent
(QuadraticDiscriminant)
: Quadratic discriminant analysis
(QuantileRegression)
: Quantile regression
(RadiusNeighbor)
: radius neighbor
(RadiusNeighborRegression)
: radius neighbor regression
(SemiSupervisedRadiusNeighbor)
: Semi-supervised radius neighbor
(RamerDouglasPeucker)
: Ramer-Douglas-Peucker algorithm
(RandomForestClassifier)
: Random forest classifier
(RandomForestRegressor)
: Random forest regressor
(RandomProjection)
: Random projection
(RANSAC)
: Random sample consensus
(RadialBasisFunctionNetwork)
: Radial basis function network
(GBRBM)
: Gaussian-Bernouili Restricted Boltzmann machine
(RBM)
: Restricted Boltzmann machine
(RBP)
: Randomized Budget Perceptron
(RDOS)
: Relative Density-based Outlier Score
(KernelRidge)
: Kernel ridge regression
(Ridge)
: Ridge regressioin
(RKOF)
: Robust Kernel-based Outlier Factor
(RecursiveLeastSquares)
: Recursive least squares
(RepeatedMedianRegression)
: Repeated median regression
(RNN)
: Recurrent neuralnetwork
(RobertsCross)
: Roberts cross
(RobustScaler)
: Robust scaler
(ROCK)
: RObust Clustering using linKs
(AggressiveROMMA)
: Aggressive Relaxed Online Maximum Margin Algorithm
(ROMMA)
: Relaxed Online Maximum Margin Algorithm
(RVM)
: Relevance vector machine
(S3VM)
: Semi-Supervised Support Vector Machine
(Sammon)
: Sammon mapping
(SARSAAgent)
: SARSA agent
(SauvolaThresholding)
: sauvola thresholding
(SavitzkyGolayFilter)
: Savitzky-Golay filter
(SDAR)
: Sequentially Discounting Autoregressive model
(SegmentedRegression)
: Segmented regression
(SelectiveNaiveBayes)
: Selective Naive bayes
(SelectiveSamplingAdaptivePerceptron)
: Selective sampling Perceptron with adaptive parameter
(SelectiveSamplingPerceptron)
: Selective sampling Perceptron
(SelectiveSamplingSOP)
: Selective sampling second-order Perceptron
(SelectiveSamplingWinnow)
: Selective sampling Winnow
(SelfTraining)
: Self-training
(SemiSupervisedNaiveBayes)
: Semi-supervised naive bayes
(SezanThresholding)
: Sezan's thresholding
(ShiftingPerceptron)
: Shifting Perceptron Algorithm
(SincInterpolation)
: Sinc interpolation
(SlicedInverseRegression)
: Sliced inverse regression
(Slerp)
: Spherical linear interpolation
(SliceSampling)
: slice sampling
(SMARegression)
: Standardizes Major Axis regression
(qt)
(SmirnovGrubbs)
: SmirnovGrubbs test
(SmoothstepInterpolation)
: Smoothstep interpolation
(Snakes)
: Snakes (active contour model)
(Sobel)
: Sobel edge detection
(SoftKMeans)
: Soft k-means
(SOM)
: Self-Organizing Map
(SecondOrderPerceptron)
: Second order perceptron
(SpectralClustering)
: Spectral clustering
(SmoothingSpline)
: Spline smoothing
(SplineInterpolation)
: Spline interpolation
(SplitAndMerge)
: Split and merge segmentation
(SquaredLossMICPD)
: Squared-loss Mutual information change point detection
(SST)
: Singular-spectrum transformation
(Standardization)
: Standardization
(StatisticalRegionMerging)
: Statistical Region Merging
(STING)
: STatistical INformation Grid-based method
(Stoptron)
: Stoptron
(SVC)
: Support vector clustering
(SVM)
: Support vector machine
(SVR)
: Support vector regression
(TheilSenRegression)
: Theil-Sen regression
(Thompson)
: Thompson test
(TietjenMoore)
: Tietjen-Moore Test
(TighterPerceptron)
: Tighter Budget Perceptron
(TightestPerceptron)
: Tightest Perceptron
(TrigonometricInterpolation)
: Trigonometric interpolation
(SNE)
: Stochastic Neighbor Embedding
(tSNE)
: T-distributed Stochastic Neighbor Embedding
(TukeyRegression)
: Tukey regression
(TukeysFences)
: Tukey's fences
(RuLSIF)
: Relative unconstrained Least-Squares Importance Fitting
(uLSIF)
: unconstrained Least-Squares Importance Fitting
(UMAP)
: Uniform Manifold Approximation and Projection
(UniversalSetNaiveBayes)
: Universal-set Naive bayes
(VAE)
: Variational Autoencoder
(VAR)
: Vector Autoregressive model
(VBGMM)
: Variational Gaussian Mixture Model
(VotedPerceptron)
: Voted-perceptron
(WeightedKMeans)
: Weighted k-means model
(WeightedKNN)
: Weighted K-Nearest Neighbor
(WeightedLeastSquares)
: Weighted least squares
(Winnow)
: Winnow
(Word2Vec)
: Word2Vec
(XGBoost)
: eXtreme Gradient Boosting regression
(XGBoostClassifier)
: eXtreme Gradient Boosting classifier
(XMeans)
: x-means
(YeoJohnson)
: Yeo-Johnson power transformation
(ZeroInflatedPoisson)
: Zero-inflated poisson
(AcrobotRLEnvironment)
: Acrobot environment
(RLEnvironmentBase)
: Base class for reinforcement learning environment
(RLIntRange)
: Integer number range state/actioin
(RLRealRange)
: Real number range state/actioin
(EmptyRLEnvironment)
: Empty environment
(BreakerRLEnvironment)
: Breaker environment
(CartPoleRLEnvironment)
: Cartpole environment
(DraughtsRLEnvironment)
: Draughts environment
(GomokuRLEnvironment)
: Gomoku environment
(GridMazeRLEnvironment)
: Grid world environment
(InHypercubeRLEnvironment)
: In-hypercube environment
(SmoothMazeRLEnvironment)
: Smooth maze environment
(MountainCarRLEnvironment)
: MountainCar environment
(PendulumRLEnvironment)
: Pendulum environment
(ReversiRLEnvironment)
: Reversi environment
(WaterballRLEnvironment)
: Waterball environment
(accuracy)
(cohensKappa)
(fScore)
(precision)
(recall)
(davisBouldinIndex)
(diceIndex)
(dunnIndex)
(fowlkesMallowsIndex)
(jaccardIndex)
(purity)
(randIndex)
(silhouetteCoefficient)
(coRankingMatrix)
(correlation)
(mad)
(mae)
(mape)
(mse)
(msle)
(r2)
(rmse)
(rmsle)
(rmspe)
A2C agent
(RLEnvironmentBase)
Environment
(number)
Resolution of actions
(number)
Number of processes
(string)
Optimizer of the network
Adaptive Linear Neuron model
(number)
Learning rate
Adaptive Metric Nearest Neighbor
(number?
= null)
The number of neighbors of the test point
(number
= 3)
The number of neighbors in N1 for estimation
(number?
= null)
The size of the neighborhood N2 for each of the k0 neighbors for estimation
(number?
= null)
The number of points within the delta intervals
(number
= 3)
The number of neighbors in the final nearest neighbor rule
(number
= 0.5)
The positive factor for the exponential weighting scheme
Adaptive thresholding
"mean" | "gaussian" | "median" | "midgray"), k: number, c: number)(("mean" | "gaussian" | "median" | "midgray")
= 'mean')
Method name
(number
= 3)
Size of local range
(number
= 2)
Value subtracted from threshold
Affinity propagation model
Type: object
(number?)
: Data index of leaf node
(number?)
: Distance between children nodes
(number)
: Number of leaf nodes
(Array<AgglomerativeClusterNode>?)
: Children nodes
(Array<AgglomerativeClusterNode>)
: Leaf nodes
Agglomerative clustering
"euclid" | "manhattan" | "chebyshev"))(("euclid" | "manhattan" | "chebyshev")
= 'euclid')
Metric name
Returns the specified number of clusters.
(number)
Number of clusters
Array<AgglomerativeClusterNode>:
Cluster nodes
Returns a distance between two nodes.
(AgglomerativeClusterNode)
Node
(AgglomerativeClusterNode)
Node
number:
Distance
Returns new distance.
(number)
Number of datas in a merging node A
(number)
Number of datas in a merging node B
(number)
Number of datas in a current node
(number)
Distance between node A and current node
(number)
Distance between node B and current node
(number)
Distance between node A and node B
number:
New distance between current node and merged node
Complete linkage agglomerative clustering
Extends AgglomerativeClustering
Returns a distance between two nodes.
(AgglomerativeClusterNode)
Node
(AgglomerativeClusterNode)
Node
number:
Distance
Returns new distance.
(number)
Number of datas in a merging node A
(number)
Number of datas in a merging node B
(number)
Number of datas in a current node
(number)
Distance between node A and current node
(number)
Distance between node B and current node
(number)
Distance between node A and node B
number:
New distance between current node and merged node
Single linkage agglomerative clustering
Extends AgglomerativeClustering
Returns a distance between two nodes.
(AgglomerativeClusterNode)
Node
(AgglomerativeClusterNode)
Node
number:
Distance
Returns new distance.
(number)
Number of datas in a merging node A
(number)
Number of datas in a merging node B
(number)
Number of datas in a current node
(number)
Distance between node A and current node
(number)
Distance between node B and current node
(number)
Distance between node A and node B
number:
New distance between current node and merged node
Group average agglomerative clustering
Extends AgglomerativeClustering
Returns a distance between two nodes.
(AgglomerativeClusterNode)
Node
(AgglomerativeClusterNode)
Node
number:
Distance
Returns new distance.
(number)
Number of datas in a merging node A
(number)
Number of datas in a merging node B
(number)
Number of datas in a current node
(number)
Distance between node A and current node
(number)
Distance between node B and current node
(number)
Distance between node A and node B
number:
New distance between current node and merged node
Ward's agglomerative clustering
Extends AgglomerativeClustering
Returns a distance between two nodes.
(AgglomerativeClusterNode)
Node
(AgglomerativeClusterNode)
Node
number:
Distance
Returns new distance.
(number)
Number of datas in a merging node A
(number)
Number of datas in a merging node B
(number)
Number of datas in a current node
(number)
Distance between node A and current node
(number)
Distance between node B and current node
(number)
Distance between node A and node B
number:
New distance between current node and merged node
Centroid agglomerative clustering
Extends AgglomerativeClustering
Returns a distance between two nodes.
(AgglomerativeClusterNode)
Node
(AgglomerativeClusterNode)
Node
number:
Distance
Returns new distance.
(number)
Number of datas in a merging node A
(number)
Number of datas in a merging node B
(number)
Number of datas in a current node
(number)
Distance between node A and current node
(number)
Distance between node B and current node
(number)
Distance between node A and node B
number:
New distance between current node and merged node
Weighted average agglomerative clustering
Extends AgglomerativeClustering
Returns a distance between two nodes.
(AgglomerativeClusterNode)
Node
(AgglomerativeClusterNode)
Node
number:
Distance
Returns new distance.
(number)
Number of datas in a merging node A
(number)
Number of datas in a merging node B
(number)
Number of datas in a current node
(number)
Distance between node A and current node
(number)
Distance between node B and current node
(number)
Distance between node A and node B
number:
New distance between current node and merged node
Median agglomerative clustering
Extends AgglomerativeClustering
Returns a distance between two nodes.
(AgglomerativeClusterNode)
Node
(AgglomerativeClusterNode)
Node
number:
Distance
Returns new distance.
(number)
Number of datas in a merging node A
(number)
Number of datas in a merging node B
(number)
Number of datas in a current node
(number)
Distance between node A and current node
(number)
Distance between node B and current node
(number)
Distance between node A and node B
number:
New distance between current node and merged node
Akima interpolation
(boolean
= false)
Use modified method or not
Approximate Large Margin algorithm
(number
= 2)
Power parameter for norm
(number
= 1)
Degree of approximation to the optimal margin hyperplane
(number
= 1)
Tuning parameter
(number
= 1)
Tuning parameter
Averaged One-Dependence Estimators
(number
= 20)
Discretized number
Autoregressive model
(number)
Order
(("lsm" | "yuleWalker" | "levinson" | "householder")
= lms)
Method name
Fit model.
Autoregressive moving average model
Fit model.
Adaptive regularization of Weight Vectors
(number
= 0.1)
Learning rate
Adaptive resonance theory
"l2")(number
= 1)
Threshold
("l2"
= 'l2')
Method name
Apriori algorithm
(number)
Minimum support
Association analysis
(number)
Minimum support
Fit model.
Autoencoder
(number)
Input size
(number)
Reduced dimension
(string)
Optimizer of the network
Automatic thresholding
Fit model.
Average shifted histogram
Balanced histogram thresholding
(number
= 500)
Minimum data count
Ballseptron
(number)
Radius
Banditron
(number
= 0.5)
Gamma
Bayesian linear regression
Bayesian Network
(number)
Equivalent sample size
Fit model.
Bernsen thresholding
Bessel filter
Extends LowpassFilter
Bilinear interpolation
Balanced iterative reducing and clustering using hierarchies
(number)
(number
= 10)
Maximum number of entries for each non-leaf nodes
(number
= 0.2)
Threshold
(number
= Infinity)
Maximum number of entries for each leaf nodes
Box-Cox transformation
(number?
= null)
Lambda
Brahmagupta interpolation
Budgeted Stochastic Gradient Descent
"removal" | "projection" | "merging"), kernel: ("gaussian" | "polynomial" | function (Array<number>, Array<number>): number))(number
= 10)
Budget size
(number
= 1)
Learning rate
(number
= 1)
Regularization parameter
(("removal" | "projection" | "merging")
= removal)
Maintenance type
Budget Perceptron
Butterworth filter
Extends LowpassFilter
Clustering based on Closest Pairs
Canny edge detection
Clustering Affinity Search Technique
(number)
Affinity threshold
Categorical naive bayes
(number
= 1.0)
Smoothing parameter
Catmull-Rom splines interpolation
Centripetal Catmull-Rom splines interpolation
(number
= 0.5)
Number for knot parameterization
CHAMELEON
(number
= 5)
Number of neighborhoods
Change finder
Chebyshev filter
Extends LowpassFilter
Clustering LARge Applications
(number)
Number of clusters
Clustering Large Applications based on RANdomized Search
(number)
Number of clusters
CLUstEring based on local Shrinking
(number
= 0.05)
Speed factor
Co-training
Connectivity-based Outlier Factor
(number)
Number of neighborhoods
Complement Naive Bayes
"gaussian")("gaussian"
= gaussian)
Distribution name
Confidence weighted
(number)
Confidence value
Soft confidence weighted
Extends ConfidenceWeighted
Cosine interpolation
Conditional random fields
Cubic-convolution interpolation
(number)
Tuning parameter
Fit model parameters.
Cubic Hermite spline
Cubic interpolation
Cumulative moving average
Cumulative sum change point detection
Type: object
Clustering Using REpresentatives
(number)
Number of representative points
Discriminant adaptive nearest neighbor
(number
= null)
Number of neighborhoods
Density-based spatial clustering of applications with noise
(number
= 0.5)
Radius to determine neighborhood
(number
= 5)
Minimum size of cluster
(("euclid" | "manhattan" | "chebyshev")
= euclid)
Metric name
Decision tree
Decision tree classifier
"ID3" | "CART"))Extends DecisionTree
(("ID3" | "CART"))
Method name
Decision tree regression
Extends DecisionTree
Delaunay interpolation
Deming regression
(number)
Ratio of variances
DENsity CLUstering
(number)
Smoothing parameter for the kernel
((1 | 2)
= 1)
Version number
DIvisive ANAlysis Clustering
Diffusion map
(number)
Power parameter
Deep Q-Network agent
(RLEnvironmentBase)
Environment
(number)
Resolution of actions
(string)
Optimizer of the network
DQN Method
(("DQN" | "DDQN"))
New method name
Dynamic programming agent
(RLEnvironmentBase)
Environment
(number
= 20)
Resolution
Elastic net
(number
= 0.1)
Regularization strength
(number
= 0.5)
Mixing parameter
(("ISTA" | "CD")
= CD)
Method name
Elliptic filter
Extends LowpassFilter
Extended Nearest Neighbor
0 | 1 | 2), k: number, metric: ("euclid" | "manhattan" | "chebyshev" | "minkowski"))((0 | 1 | 2)
= 1)
Version
(number
= 5)
Number of neighborhoods
(("euclid" | "manhattan" | "chebyshev" | "minkowski")
= euclid)
Metric name
Type: object
Ensemble binary models
Exponential moving average
Modified moving average
Bsae class for Extremely Randomized Trees
Extra trees classifier
Extends ExtraTrees
Extra trees regressor
Extends ExtraTrees
FastMap
Forgetron
"gaussian" | "polynomial" | function (Array<number>, Array<number>): number))(number)
Budget parameter
Fuzzy c-means
(number
= 2)
Fuzziness factor
Fuzzy k-nearest neighbor
Generative adversarial networks
"" | "conditional"))(number)
Number of noise dimension
(string)
Optimizer of the generator network
(string)
Optimizer of the discriminator network
((number | null))
Class size for conditional type
(("" | "conditional"))
Type name
Fit model.
(number)
Iteration count
(number)
Learning rate for generator
(number)
Learning rate for discriminator
(number)
Batch size
{generatorLoss: number, discriminatorLoss: number}:
Loss value
Gasser–Müller kernel estimator
(number)
Smoothing parameter for the kernel
Gaussian process
"gaussian", beta: number)("gaussian"
= gaussian)
Kernel name
(number
= 1)
Precision parameter
Gradient boosting decision tree
(number
= 1)
Maximum depth of tree
(number
= 1.0)
Sampling rate
(number
= 0)
Learning rate
Gradient boosting decision tree classifier
Extends GBDT
(number
= 1)
Maximum depth of tree
(number
= 1.0)
Sampling rate
(number
= 0)
Learning rate
Generalized extreme studentized deviate
Type: object
(function (...any): void)
: Run model
(function (): GeneticModel)
: Returns mutated model
(function (GeneticModel): GeneticModel)
: Returns mixed model
(function (): number)
: Returns a number how good the model is
Genetic algorithm
(number)
Number of models per generation
(any)
Models
Type: Array<GeneticModel>
The best model.
GeneticModel:
Best model
Run for all models.
(...any)
Arguments for run
Genetic algorithm generation
(RLEnvironmentBase)
Environment
(number
= 100)
Number of models per generation
(number
= 20)
Resolution
Reset all agents.
Returns the best score agent.
GeneticAlgorithmAgent:
Best agent
Run for all agents.
Genetic k-means model
G-means
Gaussian mixture model
Semi-Supervised gaussian mixture model
Extends GMM
Gaussian mixture regression
Extends GMM
Gaussian Process Latent Variable Model
"gaussian", kernelArgs: Array<any>?)(number)
Reduced dimension
(number)
Precision parameter
(number
= 1.0)
Learning rate for z
(number
= 0.005)
Learning rate for alpha
(number
= 0.2)
Learning rate for kernel
("gaussian"
= gaussian)
Kernel name
(Array<any>?
= [])
Arguments for kernel
Growing cell structures
Growing neural gas
Generative topographic mapping
(number)
Input size
(number)
Output size
(number
= 20)
Grid size
(number
= 10)
Grid size for basis function
Hampel filter
Hierarchical Density-based spatial clustering of applications with noise
(number
= 5)
Minimum number of clusters to be recognized as a cluster
(number
= 5)
Number of neighborhood with core distance
(("euclid" | "manhattan" | "chebyshev")
= euclid)
Metric name
Histogram
(object?
= {})
Config
Hessian Locally Linear Embedding
(number
= 1)
Number of neighborhoods
Hidden Markov model
(number)
Number of states
Hidden Markov model
Extends HMMBase
(number)
Number of states
Continuous hidden Markov model
Extends HMMBase
Hidden Markov model classifier
(number)
Number of states
(any
= ContinuousHMM)
HMM class
Holt-Winters method
(number)
Weight for last value
(number
= 0)
Weight for trend value
(number
= 0)
Weight for seasonal data
(number
= 0)
Length of season
Hopfield network
Hotelling T-square Method
Huber regression
(number
= 1.35)
Threshold of outliers
(("rls" | "gd")
= rls)
Method name
(number
= 1)
Learning rate
Independent component analysis
Classical ellipsoid method
Improved ellipsoid method
(number
= 0.9)
Parameter controlling the memory of online learning
(number
= 0.5)
Parameter controlling the memory of online learning
Locally Informative K-Nearest Neighbor
Incremental principal component analysis
(number
= 0.95)
Forgetting factor
Influenced Outlierness
(number)
Number of neighborhoods
Inverse distance weighting
"euclid" | "manhattan" | "chebyshev" | "minkowski"))(number
= 5)
Number of neighborhoods
(number
= 2)
Power parameter
(("euclid" | "manhattan" | "chebyshev" | "minkowski")
= euclid)
Metric name
Inverse smoothstep interpolation
Iterative Self-Organizing Data Analysis Technique
(number)
Initial cluster count
(number)
Minimum cluster count
(number)
Maximum cluster count
(number)
Minimum cluster size
(number)
Standard deviation as splid threshold
(number)
Merge distance
Isolation forest
Isomap
(number
= 0)
Number of neighborhoods
Isotonic regression
Kalman filter
Kernel Density Estimation Outlier Score
"gaussian" | "epanechnikov" | function (number, number, number): number))Kernel density estimator
"gaussian" | "rectangular" | "triangular" | "epanechnikov" | "biweight" | "triweight" | function (number): number))Kernel k-means
(number
= 3)
Number of clusters
Kernelized Primal Estimated sub-GrAdientSOlver for SVM
"gaussian" | "polynomial" | function (Array<number>, Array<number>): number))(number)
Learning rate
Kernelized perceptron
"gaussian" | "polynomial" | function (Array<number>, Array<number>): number))(number
= 1)
Learning rate
Kullback-Leibler importance estimation procedure
Bsae class for k-means like model
k-means model
Extends KMeansBase
k-means++ model
Extends KMeans
k-medoids model
Extends KMeans
k-medians model
Extends KMeans
semi-supervised k-means model
Extends KMeansBase
k-modes model
Bsae class for k-nearest neighbor models
(number
= 5)
Number of neighborhoods
(("euclid" | "manhattan" | "chebyshev" | "minkowski")
= euclid)
Metric name
k-nearest neighbor
Extends KNNBase
(number
= 5)
Number of neighborhoods
(("euclid" | "manhattan" | "chebyshev" | "minkowski")
= euclid)
Metric name
k-nearest neighbor regression
Extends KNNBase
(number
= 5)
Number of neighborhoods
(("euclid" | "manhattan" | "chebyshev" | "minkowski")
= euclid)
Metric name
k-nearest neighbor anomaly detection
Extends KNNBase
(number
= 5)
Number of neighborhoods
(("euclid" | "manhattan" | "chebyshev" | "minkowski")
= euclid)
Metric name
k-nearest neighbor density estimation
Extends KNNBase
(number
= 5)
Number of neighborhoods
(("euclid" | "manhattan" | "chebyshev" | "minkowski")
= euclid)
Metric name
Semi-supervised k-nearest neighbor
Extends KNNBase
(number
= 5)
Number of neighborhoods
(("euclid" | "manhattan" | "chebyshev" | "minkowski")
= euclid)
Metric name
k-prototypes model
(number)
Weight for categorical data
k-SVD
Kolmogorov–Zurbenko filter
Label propagation
(("rbf" | "knn")
= rbf)
Method name
(number
= 0.1)
Sigma of normal distribution
(number
= Infinity)
Number of neighborhoods
Label spreading
(number
= 0.2)
Clamping factor
(("rbf" | "knn")
= rbf)
Method name
(number
= 0.1)
Sigma of normal distribution
(number
= Infinity)
Number of neighborhoods
Ladder network
Fit model.
(Array<(any | null)>)
Target values
(number)
Iteration count
(number)
Learning rate
(number)
Batch size
{labeledLoss: number, unlabeledLoss: number}:
Loss value
Lagrange interpolation
"weighted" | "newton" | ""))(("weighted" | "newton" | "")
= weighted)
Method name
Lanczos interpolation
(number)
Order
Fit model parameters.
Laplacian eigenmaps
"rbf" | "knn"), k: number, sigma: number, laplacian: ("unnormalized" | "normalized"))(("rbf" | "knn")
= rbf)
Affinity type name
(number
= 10)
Number of neighborhoods
(number
= 1)
Sigma of normal distribution
(("unnormalized" | "normalized")
= unnormalized)
Normalized laplacian matrix or not
Laplacian edge detection
(number)
Threshold
((4 | 8)
= 4)
Number of neighborhoods
Least absolute shrinkage and selection operator
(number
= 1.0)
Regularization strength
(("CD" | "ISTA" | "LARS")
= CD)
Method name
Latent dirichlet allocation
(number
= 2)
Topic count
Linde-Buzo-Gray algorithm
Linear discriminant analysis
Fishers linear discriminant analysis
Multiclass linear discriminant analysis
Linear discriminant analysis
Local Density Factor
(number)
Number of neighborhoods
Local Distance-based Outlier Factor
(number)
Number of neighborhoods
Least absolute deviations
Least squares
Linear interpolation
Locally Linear Embedding
(number
= 1)
Number of neighborhoods
Least median squares regression
(number
= 5)
Sampling count
Large Margin Nearest Neighbor
Local Correlation Integral
(number
= 0.5)
Alpha
Local Outlier Factor
(number)
Number of neighborhoods
Laplacian of gaussian filter
(number)
Threshold
Logarithmic interpolation
Logistic regression
Multinomial logistic regression
Local Outlier Probability
(number)
Number of neighborhoods
Locally weighted scatter plot smooth
Lowpass filter
(number
= 0.5)
Cutoff rate
Lp norm linear regression
(number
= 2)
Power parameter for norm
Latent Semantic Analysis
Least-squares density difference
LSDD for change point detection
least-squares importance fitting
Least trimmed squares
(number
= 0.9)
Sampling rate
Learning Vector Quantization clustering
(number)
Number of clusters
Learning Vector Quantization classifier
1 | 2 | 3))((1 | 2 | 3))
Type number
Median Absolute Deviation
Margin Perceptron
(number)
Learning rate
Markov switching
(number)
Number of regime
Max absolute scaler
Maximum likelihood estimator
"normal")("normal"
= normal)
Distribution name
Minimum Covariance Determinant
Mixture discriminant analysis
(number)
Number of components
Multi-dimensional Scaling
Mean shift
(number)
Smoothing parameter for the kernel
Metropolis-Hastings algorithm
(number)
Output size
("gaussian"
= gaussian)
Proposal density name
Min-max normalization
Margin Infused Relaxed Algorithm
Modified Locally Linear Embedding
(number
= 1)
Number of neighborhoods
Multi layer perceptron classifier
Multi layer perceptron regressor
Method of Optimal Direction
MONothetic Analysis Clustering
Monothetic Clustering
Monte Carlo agent
(RLEnvironmentBase)
Environment
(number
= 20)
Resolution
Mountain method
Simple moving average
Linear weighted moving average
Triangular moving average
Moving median
Mahalanobis Taguchi method
Mutual information feature selector
Mutual k-nearest-neighbor model
(number
= 5)
Number of neighborhoods
n-cubic interpolation
n-linear interpolation
Nadaraya–Watson kernel regression
(number?)
Sigmas of normal distribution
Naive bayes
"gaussian")("gaussian"
= gaussian)
Distribution name
Narrow Adaptive Regularization Of Weights
(number
= 1)
Tuning parameter
Natural neighbor interpolation
Neighbourhood components analysis
Nearest centroid classifier
"euclid" | "manhattan" | "chebyshev" | "minkowski"))(("euclid" | "manhattan" | "chebyshev" | "minkowski")
= euclid)
Metric name
Negation Naive bayes
"gaussian")("gaussian"
= gaussian)
Distribution name
Neural gas model
Exception for neuralnetwork class
Extends Error
(string)
Error message
(any)
Some value
Neuralnetwork
(ComputationalGraph)
Graph of a network
(("sgd" | "adam" | "momentum" | "rmsprop")
= sgd)
Optimizer of the network
Returns neuralnetwork.
"sgd" | "adam" | "momentum" | "rmsprop")): NeuralNetwork(string?)
Loss name
(("sgd" | "adam" | "momentum" | "rmsprop")
= sgd)
Optimizer of the network
NeuralNetwork:
Created Neuralnetwork
Load onnx model.
((Uint8Array | ArrayBuffer | File))
File
Promise<NeuralNetwork>:
Loaded NeuralNetwork
Returns a copy of this.
NeuralNetwork:
Copied network
Returns calculated values.
(Matrix | Object<string, Matrix>):
Calculated values
Fit model.
(number
= 1)
Iteration count
(number
= 0.1)
Learning rate
(number?
= null)
Batch size
(object?
= {})
Option
Array<number>:
Loss value
Niblack thresholding
Flow-based generative model non-linear independent component estimation
Non-local means filter
Non-negative matrix factorization
Natural Neighborhood Based Classification Algorithm
"euclid" | "manhattan" | "chebyshev" | "minkowski"))(("euclid" | "manhattan" | "chebyshev" | "minkowski")
= euclid)
Metric name
Type: object
Computational graph for Neuralnetwork structure
Returns Graph.
ComputationalGraph:
Graph
Neuralnetwork layer
(object)
Config
Base class for loss layer
Extends Layer
Base class for Flow-based generative model
Extends Layer
ONNX importer
Load onnx model.
((Uint8Array | ArrayBuffer | File))
File
Generated by JsPbCodeGenerator.
Extends jspb.Message
(Array?)
Optional initial data array, typically from a
server response, or constructed directly in Javascript. The array is used
in place and becomes part of the constructed object. It is not cloned.
If no data is provided, the constructed object will be empty, but still
valid.
Static version of the {@see toObject} method.
((boolean | undefined))
Deprecated. Whether to include
the JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
(!proto.onnx.AttributeProto)
The msg instance to transform.
!Object:
Deserializes binary data (in protobuf wire format).
(jspb.ByteSource)
The bytes to deserialize.
!proto.onnx.AttributeProto:
Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
(!proto.onnx.AttributeProto)
The message object to deserialize into.
(!jspb.BinaryReader)
The BinaryReader to use.
!proto.onnx.AttributeProto:
Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
(!proto.onnx.AttributeProto)
(!jspb.BinaryWriter)
Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
(boolean?)
Deprecated. whether to include the
JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
!Object:
Serializes the message to binary data (in protobuf wire format).
!Uint8Array:
Clears the field making it undefined.
!proto.onnx.AttributeProto:
returns this
Clears the field making it undefined.
!proto.onnx.AttributeProto:
returns this
Clears the field making it undefined.
!proto.onnx.AttributeProto:
returns this
optional AttributeType type = 20;
!proto.onnx.AttributeProto.AttributeType:
(!proto.onnx.AttributeProto.AttributeType)
!proto.onnx.AttributeProto:
returns this
Clears the field making it undefined.
!proto.onnx.AttributeProto:
returns this
Clears the field making it undefined.
!proto.onnx.AttributeProto:
returns this
Clears the field making it undefined.
!proto.onnx.AttributeProto:
returns this
optional bytes s = 4;
!(string | Uint8Array):
optional bytes s = 4; Note that Uint8Array is not supported on all browsers.
!Uint8Array:
getS()
(!(string | Uint8Array))
!proto.onnx.AttributeProto:
returns this
Clears the field making it undefined.
!proto.onnx.AttributeProto:
returns this
optional TensorProto t = 5;
proto.onnx.TensorProto?:
Clears the message field making it undefined.
!proto.onnx.AttributeProto:
returns this
optional GraphProto g = 6;
proto.onnx.GraphProto?:
Clears the message field making it undefined.
!proto.onnx.AttributeProto:
returns this
optional SparseTensorProto sparse_tensor = 22;
proto.onnx.SparseTensorProto?:
Clears the message field making it undefined.
!proto.onnx.AttributeProto:
returns this
optional TypeProto tp = 14;
proto.onnx.TypeProto?:
Clears the message field making it undefined.
!proto.onnx.AttributeProto:
returns this
!proto.onnx.AttributeProto:
returns this
!proto.onnx.AttributeProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.AttributeProto:
returns this
!proto.onnx.AttributeProto:
returns this
!proto.onnx.AttributeProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.AttributeProto:
returns this
repeated bytes strings = 9;
!(Array<!Uint8Array> | Array<string>):
repeated bytes strings = 9; Note that Uint8Array is not supported on all browsers.
!Array<!Uint8Array>:
getStringsList()
(!(Array<!Uint8Array> | Array<string>))
!proto.onnx.AttributeProto:
returns this
(!(string | Uint8Array))
(number?)
!proto.onnx.AttributeProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.AttributeProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.AttributeProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.AttributeProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.AttributeProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.AttributeProto:
returns this
Generated by JsPbCodeGenerator.
Extends jspb.Message
(Array?)
Optional initial data array, typically from a
server response, or constructed directly in Javascript. The array is used
in place and becomes part of the constructed object. It is not cloned.
If no data is provided, the constructed object will be empty, but still
valid.
Static version of the {@see toObject} method.
((boolean | undefined))
Deprecated. Whether to include
the JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
(!proto.onnx.ValueInfoProto)
The msg instance to transform.
!Object:
Deserializes binary data (in protobuf wire format).
(jspb.ByteSource)
The bytes to deserialize.
!proto.onnx.ValueInfoProto:
Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
(!proto.onnx.ValueInfoProto)
The message object to deserialize into.
(!jspb.BinaryReader)
The BinaryReader to use.
!proto.onnx.ValueInfoProto:
Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
(!proto.onnx.ValueInfoProto)
(!jspb.BinaryWriter)
Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
(boolean?)
Deprecated. whether to include the
JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
!Object:
Serializes the message to binary data (in protobuf wire format).
!Uint8Array:
Clears the field making it undefined.
!proto.onnx.ValueInfoProto:
returns this
optional TypeProto type = 2;
proto.onnx.TypeProto?:
Clears the message field making it undefined.
!proto.onnx.ValueInfoProto:
returns this
Clears the field making it undefined.
!proto.onnx.ValueInfoProto:
returns this
Generated by JsPbCodeGenerator.
Extends jspb.Message
(Array?)
Optional initial data array, typically from a
server response, or constructed directly in Javascript. The array is used
in place and becomes part of the constructed object. It is not cloned.
If no data is provided, the constructed object will be empty, but still
valid.
Static version of the {@see toObject} method.
((boolean | undefined))
Deprecated. Whether to include
the JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
(!proto.onnx.NodeProto)
The msg instance to transform.
!Object:
Deserializes binary data (in protobuf wire format).
(jspb.ByteSource)
The bytes to deserialize.
!proto.onnx.NodeProto:
Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
(!proto.onnx.NodeProto)
The message object to deserialize into.
(!jspb.BinaryReader)
The BinaryReader to use.
!proto.onnx.NodeProto:
Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
(!proto.onnx.NodeProto)
(!jspb.BinaryWriter)
Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
(boolean?)
Deprecated. whether to include the
JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
!Object:
Serializes the message to binary data (in protobuf wire format).
!Uint8Array:
!proto.onnx.NodeProto:
returns this
!proto.onnx.NodeProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.NodeProto:
returns this
!proto.onnx.NodeProto:
returns this
!proto.onnx.NodeProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.NodeProto:
returns this
Clears the field making it undefined.
!proto.onnx.NodeProto:
returns this
Clears the field making it undefined.
!proto.onnx.NodeProto:
returns this
Clears the field making it undefined.
!proto.onnx.NodeProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.NodeProto:
returns this
Clears the field making it undefined.
!proto.onnx.NodeProto:
returns this
Generated by JsPbCodeGenerator.
Extends jspb.Message
(Array?)
Optional initial data array, typically from a
server response, or constructed directly in Javascript. The array is used
in place and becomes part of the constructed object. It is not cloned.
If no data is provided, the constructed object will be empty, but still
valid.
Static version of the {@see toObject} method.
((boolean | undefined))
Deprecated. Whether to include
the JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
(!proto.onnx.TrainingInfoProto)
The msg instance to transform.
!Object:
Deserializes binary data (in protobuf wire format).
(jspb.ByteSource)
The bytes to deserialize.
!proto.onnx.TrainingInfoProto:
Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
(!proto.onnx.TrainingInfoProto)
The message object to deserialize into.
(!jspb.BinaryReader)
The BinaryReader to use.
!proto.onnx.TrainingInfoProto:
Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
(!proto.onnx.TrainingInfoProto)
(!jspb.BinaryWriter)
Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
(boolean?)
Deprecated. whether to include the
JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
!Object:
Serializes the message to binary data (in protobuf wire format).
!Uint8Array:
optional GraphProto initialization = 1;
proto.onnx.GraphProto?:
Clears the message field making it undefined.
!proto.onnx.TrainingInfoProto:
returns this
optional GraphProto algorithm = 2;
proto.onnx.GraphProto?:
Clears the message field making it undefined.
!proto.onnx.TrainingInfoProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.TrainingInfoProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.TrainingInfoProto:
returns this
Generated by JsPbCodeGenerator.
Extends jspb.Message
(Array?)
Optional initial data array, typically from a
server response, or constructed directly in Javascript. The array is used
in place and becomes part of the constructed object. It is not cloned.
If no data is provided, the constructed object will be empty, but still
valid.
Static version of the {@see toObject} method.
((boolean | undefined))
Deprecated. Whether to include
the JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
(!proto.onnx.ModelProto)
The msg instance to transform.
!Object:
Deserializes binary data (in protobuf wire format).
(jspb.ByteSource)
The bytes to deserialize.
!proto.onnx.ModelProto:
Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
(!proto.onnx.ModelProto)
The message object to deserialize into.
(!jspb.BinaryReader)
The BinaryReader to use.
!proto.onnx.ModelProto:
Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
(!proto.onnx.ModelProto)
(!jspb.BinaryWriter)
Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
(boolean?)
Deprecated. whether to include the
JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
!Object:
Serializes the message to binary data (in protobuf wire format).
!Uint8Array:
Clears the field making it undefined.
!proto.onnx.ModelProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.ModelProto:
returns this
Clears the field making it undefined.
!proto.onnx.ModelProto:
returns this
Clears the field making it undefined.
!proto.onnx.ModelProto:
returns this
Clears the field making it undefined.
!proto.onnx.ModelProto:
returns this
Clears the field making it undefined.
!proto.onnx.ModelProto:
returns this
Clears the field making it undefined.
!proto.onnx.ModelProto:
returns this
optional GraphProto graph = 7;
proto.onnx.GraphProto?:
Clears the message field making it undefined.
!proto.onnx.ModelProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.ModelProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.ModelProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.ModelProto:
returns this
Generated by JsPbCodeGenerator.
Extends jspb.Message
(Array?)
Optional initial data array, typically from a
server response, or constructed directly in Javascript. The array is used
in place and becomes part of the constructed object. It is not cloned.
If no data is provided, the constructed object will be empty, but still
valid.
Static version of the {@see toObject} method.
((boolean | undefined))
Deprecated. Whether to include
the JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
(!proto.onnx.StringStringEntryProto)
The msg instance to transform.
!Object:
Deserializes binary data (in protobuf wire format).
(jspb.ByteSource)
The bytes to deserialize.
!proto.onnx.StringStringEntryProto:
Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
(!proto.onnx.StringStringEntryProto)
The message object to deserialize into.
(!jspb.BinaryReader)
The BinaryReader to use.
!proto.onnx.StringStringEntryProto:
Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
(!proto.onnx.StringStringEntryProto)
(!jspb.BinaryWriter)
Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
(boolean?)
Deprecated. whether to include the
JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
!Object:
Serializes the message to binary data (in protobuf wire format).
!Uint8Array:
Clears the field making it undefined.
!proto.onnx.StringStringEntryProto:
returns this
Clears the field making it undefined.
!proto.onnx.StringStringEntryProto:
returns this
Generated by JsPbCodeGenerator.
Extends jspb.Message
(Array?)
Optional initial data array, typically from a
server response, or constructed directly in Javascript. The array is used
in place and becomes part of the constructed object. It is not cloned.
If no data is provided, the constructed object will be empty, but still
valid.
Static version of the {@see toObject} method.
((boolean | undefined))
Deprecated. Whether to include
the JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
(!proto.onnx.TensorAnnotation)
The msg instance to transform.
!Object:
Deserializes binary data (in protobuf wire format).
(jspb.ByteSource)
The bytes to deserialize.
!proto.onnx.TensorAnnotation:
Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
(!proto.onnx.TensorAnnotation)
The message object to deserialize into.
(!jspb.BinaryReader)
The BinaryReader to use.
!proto.onnx.TensorAnnotation:
Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
(!proto.onnx.TensorAnnotation)
(!jspb.BinaryWriter)
Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
(boolean?)
Deprecated. whether to include the
JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
!Object:
Serializes the message to binary data (in protobuf wire format).
!Uint8Array:
Clears the field making it undefined.
!proto.onnx.TensorAnnotation:
returns this
Clears the list making it empty but non-null.
!proto.onnx.TensorAnnotation:
returns this
Generated by JsPbCodeGenerator.
Extends jspb.Message
(Array?)
Optional initial data array, typically from a
server response, or constructed directly in Javascript. The array is used
in place and becomes part of the constructed object. It is not cloned.
If no data is provided, the constructed object will be empty, but still
valid.
Static version of the {@see toObject} method.
((boolean | undefined))
Deprecated. Whether to include
the JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
(!proto.onnx.GraphProto)
The msg instance to transform.
!Object:
Deserializes binary data (in protobuf wire format).
(jspb.ByteSource)
The bytes to deserialize.
!proto.onnx.GraphProto:
Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
(!proto.onnx.GraphProto)
The message object to deserialize into.
(!jspb.BinaryReader)
The BinaryReader to use.
!proto.onnx.GraphProto:
Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
(!proto.onnx.GraphProto)
(!jspb.BinaryWriter)
Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
(boolean?)
Deprecated. whether to include the
JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
!Object:
Serializes the message to binary data (in protobuf wire format).
!Uint8Array:
Clears the list making it empty but non-null.
!proto.onnx.GraphProto:
returns this
Clears the field making it undefined.
!proto.onnx.GraphProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.GraphProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.GraphProto:
returns this
Clears the field making it undefined.
!proto.onnx.GraphProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.GraphProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.GraphProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.GraphProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.GraphProto:
returns this
Generated by JsPbCodeGenerator.
Extends jspb.Message
(Array?)
Optional initial data array, typically from a
server response, or constructed directly in Javascript. The array is used
in place and becomes part of the constructed object. It is not cloned.
If no data is provided, the constructed object will be empty, but still
valid.
Generated by JsPbCodeGenerator.
Extends jspb.Message
(Array?)
Optional initial data array, typically from a
server response, or constructed directly in Javascript. The array is used
in place and becomes part of the constructed object. It is not cloned.
If no data is provided, the constructed object will be empty, but still
valid.
Static version of the {@see toObject} method.
((boolean | undefined))
Deprecated. Whether to include
the JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
(!proto.onnx.TensorProto.Segment)
The msg instance to transform.
!Object:
Deserializes binary data (in protobuf wire format).
(jspb.ByteSource)
The bytes to deserialize.
!proto.onnx.TensorProto.Segment:
Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
(!proto.onnx.TensorProto.Segment)
The message object to deserialize into.
(!jspb.BinaryReader)
The BinaryReader to use.
!proto.onnx.TensorProto.Segment:
Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
(!proto.onnx.TensorProto.Segment)
(!jspb.BinaryWriter)
Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
(boolean?)
Deprecated. whether to include the
JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
!Object:
Serializes the message to binary data (in protobuf wire format).
!Uint8Array:
Clears the field making it undefined.
!proto.onnx.TensorProto.Segment:
returns this
Clears the field making it undefined.
!proto.onnx.TensorProto.Segment:
returns this
Static version of the {@see toObject} method.
((boolean | undefined))
Deprecated. Whether to include
the JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
(!proto.onnx.TensorProto)
The msg instance to transform.
!Object:
Deserializes binary data (in protobuf wire format).
(jspb.ByteSource)
The bytes to deserialize.
!proto.onnx.TensorProto:
Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
(!proto.onnx.TensorProto)
The message object to deserialize into.
(!jspb.BinaryReader)
The BinaryReader to use.
!proto.onnx.TensorProto:
Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
(!proto.onnx.TensorProto)
(!jspb.BinaryWriter)
Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
(boolean?)
Deprecated. whether to include the
JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
!Object:
Serializes the message to binary data (in protobuf wire format).
!Uint8Array:
!proto.onnx.TensorProto:
returns this
!proto.onnx.TensorProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.TensorProto:
returns this
Clears the field making it undefined.
!proto.onnx.TensorProto:
returns this
optional Segment segment = 3;
proto.onnx.TensorProto.Segment?:
Clears the message field making it undefined.
!proto.onnx.TensorProto:
returns this
!proto.onnx.TensorProto:
returns this
!proto.onnx.TensorProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.TensorProto:
returns this
!proto.onnx.TensorProto:
returns this
!proto.onnx.TensorProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.TensorProto:
returns this
repeated bytes string_data = 6;
!(Array<!Uint8Array> | Array<string>):
repeated bytes string_data = 6; Note that Uint8Array is not supported on all browsers.
!Array<!Uint8Array>:
getStringDataList()
(!(Array<!Uint8Array> | Array<string>))
!proto.onnx.TensorProto:
returns this
(!(string | Uint8Array))
(number?)
!proto.onnx.TensorProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.TensorProto:
returns this
!proto.onnx.TensorProto:
returns this
!proto.onnx.TensorProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.TensorProto:
returns this
Clears the field making it undefined.
!proto.onnx.TensorProto:
returns this
Clears the field making it undefined.
!proto.onnx.TensorProto:
returns this
optional bytes raw_data = 9;
!(string | Uint8Array):
optional bytes raw_data = 9; Note that Uint8Array is not supported on all browsers.
!Uint8Array:
getRawData()
(!(string | Uint8Array))
!proto.onnx.TensorProto:
returns this
Clears the field making it undefined.
!proto.onnx.TensorProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.TensorProto:
returns this
optional DataLocation data_location = 14;
!proto.onnx.TensorProto.DataLocation:
(!proto.onnx.TensorProto.DataLocation)
!proto.onnx.TensorProto:
returns this
Clears the field making it undefined.
!proto.onnx.TensorProto:
returns this
!proto.onnx.TensorProto:
returns this
!proto.onnx.TensorProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.TensorProto:
returns this
!proto.onnx.TensorProto:
returns this
!proto.onnx.TensorProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.TensorProto:
returns this
Generated by JsPbCodeGenerator.
Extends jspb.Message
(Array?)
Optional initial data array, typically from a
server response, or constructed directly in Javascript. The array is used
in place and becomes part of the constructed object. It is not cloned.
If no data is provided, the constructed object will be empty, but still
valid.
Static version of the {@see toObject} method.
((boolean | undefined))
Deprecated. Whether to include
the JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
(!proto.onnx.SparseTensorProto)
The msg instance to transform.
!Object:
Deserializes binary data (in protobuf wire format).
(jspb.ByteSource)
The bytes to deserialize.
!proto.onnx.SparseTensorProto:
Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
(!proto.onnx.SparseTensorProto)
The message object to deserialize into.
(!jspb.BinaryReader)
The BinaryReader to use.
!proto.onnx.SparseTensorProto:
Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
(!proto.onnx.SparseTensorProto)
(!jspb.BinaryWriter)
Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
(boolean?)
Deprecated. whether to include the
JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
!Object:
Serializes the message to binary data (in protobuf wire format).
!Uint8Array:
optional TensorProto values = 1;
proto.onnx.TensorProto?:
Clears the message field making it undefined.
!proto.onnx.SparseTensorProto:
returns this
optional TensorProto indices = 2;
proto.onnx.TensorProto?:
Clears the message field making it undefined.
!proto.onnx.SparseTensorProto:
returns this
!proto.onnx.SparseTensorProto:
returns this
!proto.onnx.SparseTensorProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.SparseTensorProto:
returns this
Generated by JsPbCodeGenerator.
Extends jspb.Message
(Array?)
Optional initial data array, typically from a
server response, or constructed directly in Javascript. The array is used
in place and becomes part of the constructed object. It is not cloned.
If no data is provided, the constructed object will be empty, but still
valid.
Generated by JsPbCodeGenerator.
Extends jspb.Message
(Array?)
Optional initial data array, typically from a
server response, or constructed directly in Javascript. The array is used
in place and becomes part of the constructed object. It is not cloned.
If no data is provided, the constructed object will be empty, but still
valid.
Static version of the {@see toObject} method.
((boolean | undefined))
Deprecated. Whether to include
the JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
(!proto.onnx.TensorShapeProto.Dimension)
The msg instance to transform.
!Object:
Deserializes binary data (in protobuf wire format).
(jspb.ByteSource)
The bytes to deserialize.
!proto.onnx.TensorShapeProto.Dimension:
Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
(!proto.onnx.TensorShapeProto.Dimension)
The message object to deserialize into.
(!jspb.BinaryReader)
The BinaryReader to use.
!proto.onnx.TensorShapeProto.Dimension:
Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
(!proto.onnx.TensorShapeProto.Dimension)
(!jspb.BinaryWriter)
proto.onnx.TensorShapeProto.Dimension.ValueCase:
Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
(boolean?)
Deprecated. whether to include the
JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
!Object:
Serializes the message to binary data (in protobuf wire format).
!Uint8Array:
Clears the field making it undefined.
!proto.onnx.TensorShapeProto.Dimension:
returns this
Clears the field making it undefined.
!proto.onnx.TensorShapeProto.Dimension:
returns this
Clears the field making it undefined.
!proto.onnx.TensorShapeProto.Dimension:
returns this
Static version of the {@see toObject} method.
((boolean | undefined))
Deprecated. Whether to include
the JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
(!proto.onnx.TensorShapeProto)
The msg instance to transform.
!Object:
Deserializes binary data (in protobuf wire format).
(jspb.ByteSource)
The bytes to deserialize.
!proto.onnx.TensorShapeProto:
Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
(!proto.onnx.TensorShapeProto)
The message object to deserialize into.
(!jspb.BinaryReader)
The BinaryReader to use.
!proto.onnx.TensorShapeProto:
Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
(!proto.onnx.TensorShapeProto)
(!jspb.BinaryWriter)
Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
(boolean?)
Deprecated. whether to include the
JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
!Object:
Serializes the message to binary data (in protobuf wire format).
!Uint8Array:
Clears the list making it empty but non-null.
!proto.onnx.TensorShapeProto:
returns this
Generated by JsPbCodeGenerator.
Extends jspb.Message
(Array?)
Optional initial data array, typically from a
server response, or constructed directly in Javascript. The array is used
in place and becomes part of the constructed object. It is not cloned.
If no data is provided, the constructed object will be empty, but still
valid.
Generated by JsPbCodeGenerator.
Extends jspb.Message
(Array?)
Optional initial data array, typically from a
server response, or constructed directly in Javascript. The array is used
in place and becomes part of the constructed object. It is not cloned.
If no data is provided, the constructed object will be empty, but still
valid.
Static version of the {@see toObject} method.
((boolean | undefined))
Deprecated. Whether to include
the JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
(!proto.onnx.TypeProto.Tensor)
The msg instance to transform.
!Object:
Deserializes binary data (in protobuf wire format).
(jspb.ByteSource)
The bytes to deserialize.
!proto.onnx.TypeProto.Tensor:
Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
(!proto.onnx.TypeProto.Tensor)
The message object to deserialize into.
(!jspb.BinaryReader)
The BinaryReader to use.
!proto.onnx.TypeProto.Tensor:
Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
(!proto.onnx.TypeProto.Tensor)
(!jspb.BinaryWriter)
Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
(boolean?)
Deprecated. whether to include the
JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
!Object:
Serializes the message to binary data (in protobuf wire format).
!Uint8Array:
Clears the field making it undefined.
!proto.onnx.TypeProto.Tensor:
returns this
optional TensorShapeProto shape = 2;
proto.onnx.TensorShapeProto?:
Clears the message field making it undefined.
!proto.onnx.TypeProto.Tensor:
returns this
Generated by JsPbCodeGenerator.
Extends jspb.Message
(Array?)
Optional initial data array, typically from a
server response, or constructed directly in Javascript. The array is used
in place and becomes part of the constructed object. It is not cloned.
If no data is provided, the constructed object will be empty, but still
valid.
Static version of the {@see toObject} method.
((boolean | undefined))
Deprecated. Whether to include
the JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
(!proto.onnx.TypeProto.Sequence)
The msg instance to transform.
!Object:
Deserializes binary data (in protobuf wire format).
(jspb.ByteSource)
The bytes to deserialize.
!proto.onnx.TypeProto.Sequence:
Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
(!proto.onnx.TypeProto.Sequence)
The message object to deserialize into.
(!jspb.BinaryReader)
The BinaryReader to use.
!proto.onnx.TypeProto.Sequence:
Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
(!proto.onnx.TypeProto.Sequence)
(!jspb.BinaryWriter)
Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
(boolean?)
Deprecated. whether to include the
JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
!Object:
Serializes the message to binary data (in protobuf wire format).
!Uint8Array:
optional TypeProto elem_type = 1;
proto.onnx.TypeProto?:
Clears the message field making it undefined.
!proto.onnx.TypeProto.Sequence:
returns this
Generated by JsPbCodeGenerator.
Extends jspb.Message
(Array?)
Optional initial data array, typically from a
server response, or constructed directly in Javascript. The array is used
in place and becomes part of the constructed object. It is not cloned.
If no data is provided, the constructed object will be empty, but still
valid.
Static version of the {@see toObject} method.
((boolean | undefined))
Deprecated. Whether to include
the JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
(!proto.onnx.TypeProto.Map)
The msg instance to transform.
!Object:
Deserializes binary data (in protobuf wire format).
(jspb.ByteSource)
The bytes to deserialize.
!proto.onnx.TypeProto.Map:
Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
(!proto.onnx.TypeProto.Map)
The message object to deserialize into.
(!jspb.BinaryReader)
The BinaryReader to use.
!proto.onnx.TypeProto.Map:
Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
(!proto.onnx.TypeProto.Map)
(!jspb.BinaryWriter)
Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
(boolean?)
Deprecated. whether to include the
JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
!Object:
Serializes the message to binary data (in protobuf wire format).
!Uint8Array:
Clears the field making it undefined.
!proto.onnx.TypeProto.Map:
returns this
optional TypeProto value_type = 2;
proto.onnx.TypeProto?:
Clears the message field making it undefined.
!proto.onnx.TypeProto.Map:
returns this
Generated by JsPbCodeGenerator.
Extends jspb.Message
(Array?)
Optional initial data array, typically from a
server response, or constructed directly in Javascript. The array is used
in place and becomes part of the constructed object. It is not cloned.
If no data is provided, the constructed object will be empty, but still
valid.
Static version of the {@see toObject} method.
((boolean | undefined))
Deprecated. Whether to include
the JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
(!proto.onnx.TypeProto.Optional)
The msg instance to transform.
!Object:
Deserializes binary data (in protobuf wire format).
(jspb.ByteSource)
The bytes to deserialize.
!proto.onnx.TypeProto.Optional:
Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
(!proto.onnx.TypeProto.Optional)
The message object to deserialize into.
(!jspb.BinaryReader)
The BinaryReader to use.
!proto.onnx.TypeProto.Optional:
Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
(!proto.onnx.TypeProto.Optional)
(!jspb.BinaryWriter)
Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
(boolean?)
Deprecated. whether to include the
JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
!Object:
Serializes the message to binary data (in protobuf wire format).
!Uint8Array:
optional TypeProto elem_type = 1;
proto.onnx.TypeProto?:
Clears the message field making it undefined.
!proto.onnx.TypeProto.Optional:
returns this
Generated by JsPbCodeGenerator.
Extends jspb.Message
(Array?)
Optional initial data array, typically from a
server response, or constructed directly in Javascript. The array is used
in place and becomes part of the constructed object. It is not cloned.
If no data is provided, the constructed object will be empty, but still
valid.
Static version of the {@see toObject} method.
((boolean | undefined))
Deprecated. Whether to include
the JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
(!proto.onnx.TypeProto.SparseTensor)
The msg instance to transform.
!Object:
Deserializes binary data (in protobuf wire format).
(jspb.ByteSource)
The bytes to deserialize.
!proto.onnx.TypeProto.SparseTensor:
Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
(!proto.onnx.TypeProto.SparseTensor)
The message object to deserialize into.
(!jspb.BinaryReader)
The BinaryReader to use.
!proto.onnx.TypeProto.SparseTensor:
Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
(!proto.onnx.TypeProto.SparseTensor)
(!jspb.BinaryWriter)
Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
(boolean?)
Deprecated. whether to include the
JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
!Object:
Serializes the message to binary data (in protobuf wire format).
!Uint8Array:
Clears the field making it undefined.
!proto.onnx.TypeProto.SparseTensor:
returns this
optional TensorShapeProto shape = 2;
proto.onnx.TensorShapeProto?:
Clears the message field making it undefined.
!proto.onnx.TypeProto.SparseTensor:
returns this
Static version of the {@see toObject} method.
((boolean | undefined))
Deprecated. Whether to include
the JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
(!proto.onnx.TypeProto)
The msg instance to transform.
!Object:
Deserializes binary data (in protobuf wire format).
(jspb.ByteSource)
The bytes to deserialize.
!proto.onnx.TypeProto:
Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
(!proto.onnx.TypeProto)
The message object to deserialize into.
(!jspb.BinaryReader)
The BinaryReader to use.
!proto.onnx.TypeProto:
Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
(!proto.onnx.TypeProto)
(!jspb.BinaryWriter)
proto.onnx.TypeProto.ValueCase:
Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
(boolean?)
Deprecated. whether to include the
JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
!Object:
Serializes the message to binary data (in protobuf wire format).
!Uint8Array:
optional Tensor tensor_type = 1;
proto.onnx.TypeProto.Tensor?:
Clears the message field making it undefined.
!proto.onnx.TypeProto:
returns this
optional Sequence sequence_type = 4;
proto.onnx.TypeProto.Sequence?:
Clears the message field making it undefined.
!proto.onnx.TypeProto:
returns this
optional Map map_type = 5;
proto.onnx.TypeProto.Map?:
Clears the message field making it undefined.
!proto.onnx.TypeProto:
returns this
optional Optional optional_type = 9;
proto.onnx.TypeProto.Optional?:
Clears the message field making it undefined.
!proto.onnx.TypeProto:
returns this
optional SparseTensor sparse_tensor_type = 8;
proto.onnx.TypeProto.SparseTensor?:
Clears the message field making it undefined.
!proto.onnx.TypeProto:
returns this
Clears the field making it undefined.
!proto.onnx.TypeProto:
returns this
Generated by JsPbCodeGenerator.
Extends jspb.Message
(Array?)
Optional initial data array, typically from a
server response, or constructed directly in Javascript. The array is used
in place and becomes part of the constructed object. It is not cloned.
If no data is provided, the constructed object will be empty, but still
valid.
Static version of the {@see toObject} method.
((boolean | undefined))
Deprecated. Whether to include
the JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
(!proto.onnx.OperatorSetIdProto)
The msg instance to transform.
!Object:
Deserializes binary data (in protobuf wire format).
(jspb.ByteSource)
The bytes to deserialize.
!proto.onnx.OperatorSetIdProto:
Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
(!proto.onnx.OperatorSetIdProto)
The message object to deserialize into.
(!jspb.BinaryReader)
The BinaryReader to use.
!proto.onnx.OperatorSetIdProto:
Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
(!proto.onnx.OperatorSetIdProto)
(!jspb.BinaryWriter)
Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
(boolean?)
Deprecated. whether to include the
JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
!Object:
Serializes the message to binary data (in protobuf wire format).
!Uint8Array:
Clears the field making it undefined.
!proto.onnx.OperatorSetIdProto:
returns this
Clears the field making it undefined.
!proto.onnx.OperatorSetIdProto:
returns this
Generated by JsPbCodeGenerator.
Extends jspb.Message
(Array?)
Optional initial data array, typically from a
server response, or constructed directly in Javascript. The array is used
in place and becomes part of the constructed object. It is not cloned.
If no data is provided, the constructed object will be empty, but still
valid.
Static version of the {@see toObject} method.
((boolean | undefined))
Deprecated. Whether to include
the JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
(!proto.onnx.FunctionProto)
The msg instance to transform.
!Object:
Deserializes binary data (in protobuf wire format).
(jspb.ByteSource)
The bytes to deserialize.
!proto.onnx.FunctionProto:
Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
(!proto.onnx.FunctionProto)
The message object to deserialize into.
(!jspb.BinaryReader)
The BinaryReader to use.
!proto.onnx.FunctionProto:
Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
(!proto.onnx.FunctionProto)
(!jspb.BinaryWriter)
Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
(boolean?)
Deprecated. whether to include the
JSPB instance for transitional soy proto support:
http://goto/soy-param-migration
!Object:
Serializes the message to binary data (in protobuf wire format).
!Uint8Array:
Clears the field making it undefined.
!proto.onnx.FunctionProto:
returns this
!proto.onnx.FunctionProto:
returns this
!proto.onnx.FunctionProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.FunctionProto:
returns this
!proto.onnx.FunctionProto:
returns this
!proto.onnx.FunctionProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.FunctionProto:
returns this
!proto.onnx.FunctionProto:
returns this
!proto.onnx.FunctionProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.FunctionProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.FunctionProto:
returns this
Clears the field making it undefined.
!proto.onnx.FunctionProto:
returns this
Clears the list making it empty but non-null.
!proto.onnx.FunctionProto:
returns this
Clears the field making it undefined.
!proto.onnx.FunctionProto:
returns this
Type: number
Type: number
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: !proto.onnx.AttributeProto.AttributeType
Type: !proto.onnx.TensorProto.DataLocation
Type: number
Type: number
Type: number
Type: number
Type: number
Type: number
Type: number
Type: number
Type: !Uint8Array
Type: !Uint8Array
Type: !Uint8Array
Type: !Uint8Array
Type: string
Type: string
Type: string
Type: !proto.onnx.AttributeProto.AttributeType
Type: number
Type: number
Type: !(string | Uint8Array)
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: number
Type: string
Type: string
Type: string
Type: number
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: number
Type: string
Type: string
Type: !(string | Uint8Array)
Type: !proto.onnx.TensorProto.DataLocation
Type: number
Type: number
Type: number
Type: string
Type: string
Type: string
Type: number
Type: number
Type: number
Type: string
Type: number
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: !proto.onnx.AttributeProto.AttributeType
Type: number
Type: !(string | Uint8Array)
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: number
Type: string
Type: string
Type: string
Type: number
Type: string
Type: string
Type: string
Type: string
Type: string
Type: string
Type: number
Type: number
Type: number
Type: string
Type: string
Type: !(string | Uint8Array)
Type: !proto.onnx.TensorProto.DataLocation
Type: number
Type: string
Type: string
Type: number
Type: number
Type: number
Type: string
Type: string
Type: number
Type: string
Type: string
Type: string
Type: number
Type: string
Type: string
Type: !Uint8Array
Type: !Uint8Array
Type: proto.onnx.TensorProto?
Type: proto.onnx.GraphProto?
Type: proto.onnx.SparseTensorProto?
Type: proto.onnx.TypeProto?
Type: proto.onnx.TypeProto?
Type: proto.onnx.GraphProto?
Type: proto.onnx.GraphProto?
Type: proto.onnx.GraphProto?
Type: proto.onnx.TensorProto.Segment?
Type: proto.onnx.TensorProto?
Type: proto.onnx.TensorProto?
Type: proto.onnx.TensorShapeProto?
Type: proto.onnx.TypeProto?
Type: proto.onnx.TypeProto?
Type: proto.onnx.TypeProto?
Type: proto.onnx.TensorShapeProto?
Type: proto.onnx.TypeProto.Tensor?
Type: proto.onnx.TypeProto.Sequence?
Type: proto.onnx.TypeProto.Map?
Type: proto.onnx.TypeProto.Optional?
Type: proto.onnx.TypeProto.SparseTensor?
Type: !(Array<!Uint8Array> | Array<string>)
Type: !(Array<!Uint8Array> | Array<string>)
Type: !Array<!Uint8Array>
Type: !Array<!Uint8Array>
Type: !Array<!proto.onnx.TensorProto>
Type: !Array<!proto.onnx.GraphProto>
Type: !Array<!proto.onnx.SparseTensorProto>
Type: !Array<!proto.onnx.TypeProto>
Type: !Array<!proto.onnx.AttributeProto>
Type: !Array<!proto.onnx.StringStringEntryProto>
Type: !Array<!proto.onnx.StringStringEntryProto>
Type: !Array<!proto.onnx.OperatorSetIdProto>
Type: !Array<!proto.onnx.StringStringEntryProto>
Type: !Array<!proto.onnx.TrainingInfoProto>
Type: !Array<!proto.onnx.FunctionProto>
Type: !Array<!proto.onnx.StringStringEntryProto>
Type: !Array<!proto.onnx.NodeProto>
Type: !Array<!proto.onnx.TensorProto>
Type: !Array<!proto.onnx.SparseTensorProto>
Type: !Array<!proto.onnx.ValueInfoProto>
Type: !Array<!proto.onnx.ValueInfoProto>
Type: !Array<!proto.onnx.ValueInfoProto>
Type: !Array<!proto.onnx.TensorAnnotation>
Type: !Array<!proto.onnx.StringStringEntryProto>
Type: !Array<!proto.onnx.TensorShapeProto.Dimension>
Type: !Array<!proto.onnx.NodeProto>
Type: !Array<!proto.onnx.OperatorSetIdProto>
Type: number
Type: number
Type: number
Type: number
Type: proto.onnx.TensorShapeProto.Dimension.ValueCase
Type: proto.onnx.TypeProto.ValueCase
Handle abs operator
Handle acos operator
Handle acosh operator
Handle add operator
Handle asin operator
Handle asinh operator
Handle atan operator
Handle atanh operator
Handle averagepool operator
Handle concat operator
Handle constant operator
Handle conv operator
Handle cos operator
Handle cosh operator
Handle div operator
Handle dropout operator
Handle elu operator
Handle exp operator
Handle gemm operator
Handle input node
Handle leakyrelu operator
Handle log operator
Handle lrn operator
Handle matmul operator
Handle maxpool operator
Handle mul operator
Handle neg operator
Handle output node
Handle prelu operator
Handle relu operator
Handle reshape operator
Handle sigmoid operator
Handle sin operator
Handle sinh operator
Handle softmax operator
Handle softplus operator
Handle softsign operator
Handle sqrt operator
Handle sub operator
Handle tan operator
Handle tanh operator
Handle transpose operator
Return Tensor value.
(onnx.TensorProto.AsObject)
TensorProto
Return attribute value.
(onnx.AttributeProto.AsObject)
AttributeProto
any:
Attribute value
Array<object>:
Require layer objects
Normal Herd
(("full" | "exact" | "project" | "drop")
= exact)
Method name
(number
= 0.1)
Tradeoff value between passiveness and aggressiveness
One-class support vector machine
"gaussian" | "linear" | function (Array<number>, Array<number>): number), kernelArgs: Array<any>?)Outlier Detection using Indegree Number
Online gradient descent
"zero_one")(number
= 1)
Tuning parameter
("zero_one"
= zero_one)
Loss type name
Ordering points to identify the clustering structure
(number
= Infinity)
Radius to determine neighborhood
(number
= 5)
Number of neighborhood with core distance
(("euclid" | "manhattan" | "chebyshev")
= euclid)
Metric name
Otus's thresholding
Partitioning Around Medoids
(number)
Number of clusters
Particle filter
Passing-Bablok method
Passive Aggressive
0 | 1 | 2))((0 | 1 | 2)
= 0)
Version number
Perceptron Algorithm with Uneven Margins
Principal component analysis
Dual Principal component analysis
Kernel Principal component analysis
"gaussian" | "polynomial" | function (Array<number>, Array<number>): number), kernelArgs: Array<any>?)(Array<any>?
= [])
Arguments for kernel
Principal component analysis for anomaly detection
Extends PCA
Possibilistic c-means
(number
= 2)
Fuzziness factor
Principal component regression
Primal Estimated sub-GrAdientSOlver for SVM
(number)
Learning rate
Percentile anomaly detection
(number)
Percentile value
(("data" | "normal")
= data)
Distribution name
Perceptron
(number)
Learning rate
Averaged perceptron
(number)
Learning rate
Multiclass perceptron
(number)
Learning rate
Phansalkar thresholding
(number
= 3)
Size of local range
(number
= 0.25)
Tuning parameter
(number
= 0.5)
Tuning parameter
(number
= 2)
Tuning parameter
(number
= 10)
Tuning parameter
Partial least squares regression
(number)
Limit on the number of latent factors
Probabilistic latent semantic analysis
(number
= 2)
Number of clusters
Poisson regression
(number)
Learning rate
Policy gradient agent
(RLEnvironmentBase)
Environment
(number
= 20)
Resolution
Polynomial histogram
Polynomial interpolation
Projection pursuit regression
(number
= 5)
Number of functions
Prewitt edge detection
(number)
Threshold
Priestley–Chao kernel estimator
(number)
Smoothing parameter for the kernel
Principal curves
Probabilistic Principal component analysis
(("analysis" | "em" | "bayes")
= analysis)
Method name
(number)
Reduced dimension
Probit
Multinomial probit
Extends Probit
Projectron
"gaussian" | "polynomial" | function (Array<number>, Array<number>): number))(number
= 0)
Threshold
Projectron++
"gaussian" | "polynomial" | function (Array<number>, Array<number>): number))(number
= 0)
Threshold
P-tile thresholding
(number
= 0.5)
Percentile value
Base class for Q-table
(RLEnvironmentBase)
Environment
(number
= 20)
Resolution
States
Type: Array<(Array<any> | RLRealRange | RLIntRange)>
Actions
Type: Array<(Array<any> | RLRealRange | RLIntRange)>
Q-learning agent
(RLEnvironmentBase)
Environment
(number
= 20)
Resolution
Quadratic discriminant analysis
Quantile regression
(number
= 0.5)
Quantile value
Bsae class for radius neighbor models
(number
= 1)
Radius to determine neighborhood
(("euclid" | "manhattan" | "chebyshev" | "minkowski")
= euclid)
Metric name
radius neighbor
Extends RadiusNeighborBase
(number
= 1)
Radius to determine neighborhood
(("euclid" | "manhattan" | "chebyshev" | "minkowski")
= euclid)
Metric name
radius neighbor regression
"euclid" | "manhattan" | "chebyshev" | "minkowski"))Extends RadiusNeighborBase
(number
= 1)
Radius to determine neighborhood
(("euclid" | "manhattan" | "chebyshev" | "minkowski")
= euclid)
Metric name
Semi-supervised radius neighbor
"euclid" | "manhattan" | "chebyshev" | "minkowski"))Extends RadiusNeighborBase
(number
= 5)
Radius to determine neighborhood
(("euclid" | "manhattan" | "chebyshev" | "minkowski")
= euclid)
Metric name
Ramer-Douglas-Peucker algorithm
(number
= 0.1)
Threshold of distance
Bsae class for random forest models
(number)
Number of trees
(number
= 0.8)
Sampling rate
(DecisionTree)
Tree class
(Array<any>?
= null)
Arguments for constructor of tree class
Random forest classifier
Extends RandomForest
(number)
Number of trees
(number
= 0.8)
Sampling rate
(("ID3" | "CART")
= CART)
Method name
Random forest regressor
Extends RandomForest
Random projection
"uniform" | "root3" | "normal"))(("uniform" | "root3" | "normal")
= uniform)
Initialize method name
Type: object
Random sample consensus
(any)
((number | null)
= null)
Sampling rate
Radial basis function network
"linear" | "gaussian" | "multiquadric" | "inverse quadratic" | "inverse multiquadric" | "thin plate" | "bump"), e: number, l: number)(("linear" | "gaussian" | "multiquadric" | "inverse quadratic" | "inverse multiquadric" | "thin plate" | "bump")
= linear)
RBF name
(number
= 1)
Tuning parameter
(number
= 0)
Regularization parameter
Restricted Boltzmann machine
Gaussian-Bernouili Restricted Boltzmann machine
(number)
Size of hidden layer
(number
= 0.01)
Learning rate
(boolean
= false)
Do not learn sigma or not
Randomized Budget Perceptron
(number)
Number of support vectors
Relative Density-based Outlier Score
Ridge regressioin
(number
= 0.1)
Regularization strength
Kernel ridge regression
"gaussian" | function (Array<number>, Array<number>): number))(number
= 0.1)
Regularization strength
Robust Kernel-based Outlier Factor
"gaussian" | "epanechnikov" | "volcano" | function (Array<number>): number))(number)
Number of neighborhoods
(number)
Smoothing parameter
(number)
Sensitivity parameter
Recursive least squares
Repeated median regression
Recurrent neuralnetwork
"rnn" | "lstm" | "gru"), window: number, unit: number, out_size: number, optimizer: string)(("rnn" | "lstm" | "gru")
= lstm)
Method name
(number
= 10)
Window size
(number
= 10)
Size of recurrent unit
(number
= 1)
Output size
(string
= adam)
Optimizer of the network
Method
Type:
("rnn" | "lstm" | "gru")
Roberts cross
(number)
Threshold
Robust scaler
Type: object
RObust Clustering using linKs
(number)
Threshold
Relaxed Online Maximum Margin Algorithm
Aggressive Relaxed Online Maximum Margin Algorithm
Extends ROMMA
Relevance vector machine
Semi-Supervised Support Vector Machine
"gaussian" | "linear" | function (Array<number>, Array<number>): number), kernelArgs: Array<any>?)(Array<any>?
= [])
Arguments for kernel
Sammon mapping
SARSA agent
(RLEnvironmentBase)
Environment
(number
= 20)
Resolution
sauvola thresholding
(number
= 3)
Size of local range
(number
= 0.1)
Tuning parameter
(number
= 1)
Tuning parameter
Savitzky-Golay filter
(number)
Number of coefficients
Sequentially Discounting Autoregressive model
Segmented regression
(number
= 3)
Number of segments
Selective Naive bayes
"gaussian")("gaussian"
= gaussian)
Distribution name
Selective sampling Perceptron
Selective sampling Perceptron with adaptive parameter
Selective sampling second-order Perceptron
(number)
Smooth parameter
Selective sampling Winnow
Self-training
Semi-supervised naive bayes
(number
= 1)
Weight applied to the contribution of the unlabeled data
Sezan's thresholding
(number
= 0.5)
Tradeoff value between black and white
(number
= 5)
Sigma of normal distribution
Shifting Perceptron Algorithm
(number)
Rate of weight decay
Sinc interpolation
Fit model parameters.
Sliced inverse regression
(number)
Number of slices
Spherical linear interpolation
(number
= 1)
Angle subtended by the arc
slice sampling
Standardizes Major Axis regression
SmirnovGrubbs test
(number)
Significance level
Smoothstep interpolation
(number
= 1)
Order
Snakes (active contour model)
(number)
Penalty for length
(number)
Penalty for curvature
(number)
Penalty for conformity with image
(number
= 100)
Number of vertices
Sobel edge detection
(number)
Threshold
Soft k-means
(number
= 1)
Tuning parameter
Self-Organizing Map
(number)
Input size
(number)
Output size
(number
= 20)
Resolution of output
Second order perceptron
(number
= 1)
Tuning parameter
Spectral clustering
(("rbf" | "knn")
= rbf)
Affinity type name
(object
= {})
Config
Add a new cluster.
Clear all clusters.
Spline interpolation
Spline smoothing
(number)
Smoothing parameter
Split and merge segmentation
(("variance" | "uniformity")
= variance)
Method name
(number
= 0.1)
Threshold
Squared-loss Mutual information change point detection
(object)
Density ratio estimation model
(number)
Window size
(number?)
Take number
(number?)
Lag
Singular-spectrum transformation
Standardization
(number
= 0)
Delta Degrees of Freedom
Statistical Region Merging
(number)
Threshold
STatistical INformation Grid-based method
Stoptron
"gaussian" | "polynomial" | function (Array<number>, Array<number>): number))(number
= 10)
Cachs size
Support vector clustering
"gaussian" | "linear" | function (Array<number>, Array<number>): number), kernelArgs: Array<any>?)(Array<any>?
= [])
Arguments for kernel
Support vector machine
"gaussian" | "linear" | function (Array<number>, Array<number>): number), kernelArgs: Array<any>?)(Array<any>?
= [])
Arguments for kernel
Support vector regression
"gaussian" | "linear" | function (Array<number>, Array<number>): number), kernelArgs: Array<any>?)(Array<any>?
= [])
Arguments for kernel
Theil-Sen regression
Thompson test
(number)
Significance level
Tietjen-Moore Test
(number)
Number of outliers
Tighter Budget Perceptron
(number
= 0)
Margine
(number
= 0)
Cachs size
(("perceptron" | "mira" | "nobias")
= perceptron)
Update rule
Tightest Perceptron
"gaussian" | "polynomial" | function (Array<number>, Array<number>): number), accuracyLoss: ("zero_one" | "hinge"))Trigonometric interpolation
Stochastic Neighbor Embedding
T-distributed Stochastic Neighbor Embedding
Tukey regression
(number)
Error tolerance
Tukey's fences
(number)
Tuning parameter
Relative unconstrained Least-Squares Importance Fitting
unconstrained Least-Squares Importance Fitting
Extends RuLSIF
Uniform Manifold Approximation and Projection
(number)
Reduced dimension
(number
= 10)
Number of neighborhoods
(number
= 0.1)
Minimum distance
Universal-set Naive bayes
"gaussian")("gaussian"
= gaussian)
Distribution name
Variational Autoencoder
"" | "conditional"))(number)
Input size
(number)
Number of noise dimension
(string)
Optimizer of the network
((number | null))
Class size for conditional type
(("" | "conditional"))
Type name
Vector Autoregressive model
(number)
Order
Variational Gaussian Mixture Model
Voted-perceptron
(number
= 1)
Learning rate
Weighted k-means model
(number)
Tuning parameter
Weighted K-Nearest Neighbor
"euclid" | "manhattan" | "chebyshev" | "minkowski"), weight: ("gaussian" | "rectangular" | "triangular" | "epanechnikov" | "quartic" | "triweight" | "cosine" | "inversion"))(number)
Number of neighbors
(("euclid" | "manhattan" | "chebyshev" | "minkowski")
= euclid)
Metric name
(("gaussian" | "rectangular" | "triangular" | "epanechnikov" | "quartic" | "triweight" | "cosine" | "inversion")
= gaussian)
Weighting scheme name
Weighted least squares
Winnow
(boolean
= 2)
Learning rate
(number?
= null)
Threshold
((1 | 2)
= 1)
Version of model
Word2Vec
"CBOW" | "skip-gram"), n: number, wordsOrNumber: (number | Array<string>), reduce_size: number, optimizer: string)(("CBOW" | "skip-gram"))
Method name
(number)
Number of how many adjacent words to learn
(number)
Reduced dimension
(string)
Optimizer of the network
eXtreme Gradient Boosting regression
(number
= 1)
Maximum depth of tree
(number
= 1.0)
Sampling rate
(number
= 0.1)
Regularization parameter
(number
= 0.5)
Learning rate
eXtreme Gradient Boosting classifier
Extends XGBoost
(number
= 1)
Maximum depth of tree
(number
= 1.0)
Sampling rate
(number
= 0.1)
Regularization parameter
(number
= 0)
Learning rate
x-means
Yeo-Johnson power transformation
(number?
= null)
Lambda
Zero-inflated poisson
Acrobot environment
Extends RLEnvironmentBase
Real number range state/actioin
(number)
Minimum value
(number)
Maximum value
(("equal" | "log")
= 'equal')
Space type
Integer number range state/actioin
Base class for reinforcement learning environment
(Array<(Array<any> | RLRealRange | RLIntRange)>)
: Action variables
(Array<(Array<any> | RLRealRange | RLIntRange)>)
: States variables
Close environment.
Reset environment.
Do actioin without changing environment and returns new state.
Empty environment
Extends RLEnvironmentBase
Breaker environment
Extends RLEnvironmentBase
Cartpole environment
Extends RLEnvironmentBase
Draughts environment
Extends RLEnvironmentBase
Gomoku environment
Extends RLEnvironmentBase
Grid world environment
Extends RLEnvironmentBase
In-hypercube environment
Extends RLEnvironmentBase
(number
= 2)
Dimension of the environment
Smooth maze environment
Extends RLEnvironmentBase
MountainCar environment
Extends RLEnvironmentBase
Pendulum environment
Extends RLEnvironmentBase
Reversi environment
Extends RLEnvironmentBase
Waterball environment
Extends RLEnvironmentBase
Complex number
Exception for graph class
Extends Error
(string)
Error message
(any)
Some value
Graph class
Return degree of the node.
(number)
Index of target node
((boolean | "in" | "out")
= true)
Count undirected edges. If
in
or
out
is specified, only direct edges are counted and
direct
parameter is ignored.
((boolean | "in" | "out")
= true)
Count directed edges
number:
Degree of the node
Return indexes of adjacency nodes.
"in" | "out"), direct: (boolean | "in" | "out")): number(number)
Index of target node
((boolean | "in" | "out")
= true)
Check undirected edges. If
in
or
out
is specified, only direct edges are checked and
direct
parameter is ignored.
((boolean | "in" | "out")
= true)
Check directed edges
number:
Indexes of adjacency nodes
Returns indexes of each components.
Add the node.
(unknown?)
Value of the node
Add the edge.
Returns the edges.
(number)
Index of the starting node of the edge
(number)
Index of the end node of the edge
((boolean | null)
= null)
null
to get direct and undirect edges,
true
to get only direct edges,
false
to get only undirect edges.
Array<Edge>:
Edges between
from
to
to
Remove the edges.
Returns adjacency matrix
Returns degree matrix.
"both" | "in" | "out"))(("both" | "in" | "out")
= both)
Indegree or outdegree
Returns laplacian matrix.
Contract this graph.
Subdivision this graph.
Substitute other graph at the node.
Returns shortest path with breadth first search algorithm.
(number)
Index of start node
Array<{length: number, prev: number, path: Array<number>}>:
Shortest length and path for all nodes
Returns shortest path with Floyd–Warshall algorithm.
Returns minimum cut.
Returns minimum cut.
Returns minimum cut.
Returns bisection cut.
Exception for matrix class
Extends Error
(string)
Error message
(any)
Some value
Matrix class
Sizes of the matrix.
Elements in the matrix.
Iterate over the elements.
Set a value at the position.
Reshape this.
Concatenate this and m.
Returns a matrix reduced along the axis with the callback function.
(any?)
Initial value
(number
= -1)
Axis to be reduced. If negative, reduce along all elements.
(boolean
= null)
Keep dimensions or not. If null, negative axis retuns number and other axis returns Matrix.
(Matrix | number):
Reduced matrix or value
Multiply all elements by -1 in-place.
Set all elements to their logical NOT values.
Set all elements to their bitwise NOT values.
Set all elements to their absolute values.
Set all elements to their rounded values.
Set all elements to their floored values.
Set all elements to their ceil values.
Add a value or matrix.
Subtract a value or matrix.
Subtract this matrix from a value or matrix.
Multiplies by a value element-wise.
Divides by a value element-wise.
Divides a value by this matrix element-wise.
Take a remainder divided by a value element-wise.
Take a remainder divided a value by this matrix element-wise.
Take a logical AND with a value or matrix.
Take a logical OR with a value or matrix.
Take a bitwise AND with a value or matrix.
Take a bitwise OR with a value or matrix.
Take a bitwise XOR with a value or matrix.
Tensor class
Sizes of the tensor.
Elements in the tensor.
Iterate over the elements.
Returns a Matrix if the dimension of this tensor is 2.
Matrix:
Matrix
Concatenate this and t.
Returns a tensor reduced along the axis with the callback function.
(any?)
Initial value
(number
= -1)
Axis to be reduced. If negative, reduce along all elements.
(boolean
= false)
Keep dimensions or not.
(Tensor | number):
Reduced tensor or value